Power analysis for regression models which test the interaction of two or three independent variables on a single dependent variable. Includes options for correlated interacting variables and specifying variable reliability. Two-way interactions can include continuous, binary, or ordinal variables. Power analyses can be done either analytically or via simulation. Includes tools for simulating single data sets and visualizing power analysis results. The primary functions are power_interaction_r2() and power_interaction() for two-way interactions, and power_interaction_3way_r2() for three-way interactions. Please cite as: Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR, Olino TM (2023). "Tutorial: Power analyses for interaction effects in cross-sectional regressions." <doi:10.1177/25152459231187531>.
Version: 0.2.2 Depends: R (≥ 3.5.0) Imports: dplyr, parallel, doParallel, foreach, ggplot2, polynom, chngpt, rlang, tidyr, stats, ggbeeswarm, Matrix Published: 2024-07-09 DOI: 10.32614/CRAN.package.InteractionPoweR Author: David Baranger [aut, cre] (website: davidbaranger.com), Brandon Goldstein [ctb], Megan Finsaas [ctb], Thomas Olino [ctb], Colin Vize [ctb], Don Lynam [ctb] Maintainer: David Baranger <dbaranger at gmail.com> BugReports: https://github.com/dbaranger/InteractionPoweR/issues License: GPL (≥ 3) URL: https://dbaranger.github.io/InteractionPoweR/, https://doi.org/10.1177/25152459231187531 NeedsCompilation: no Citation: InteractionPoweR citation info Materials: README NEWS CRAN checks: InteractionPoweR results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=InteractionPoweR to link to this page.
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